For the generation of synthetic ALS-based AGB predicted patches from Sentinel-1A data, we perform two experiments. In the first experiment we evaluate the speckle filtered dataset against the dataset without speckle filtering, in addition to evaluating the three discriminator networks in Sec. REF . In the second experiment we settle for one of the datasets and discriminator networks from the first experiment. We then train nine different cGAN generator architectures (combining the three ResNet networks and the three objective functions from Sec. ) and compare their performance. In our experiments, we apply batch normalisation (BN) or instance normalisation (IN) for Eq. REF  and Eq. REF , while for Eq. REF  we apply layer normalisation (LN) for \({D}\)  and either BN or IN for the \({G}\)  network, as suggested in [1]}. We additionally experiment with batch sizes (BS) between 1 and 4. For both experiments we train the different architectures for 200 epochs, with a learning rate of \(2\times 10^{-4}\) . After training, the performance of the different models was evaluated against each other and against the 88 ground reference AGB plots.
